We apply algorithmic information theory to quantum mechanics in order to shed light on an algorithmic structure which inheres in quantum mechanics. There are two equivalent ways to define the (classical) Kolmogorov complexity K(s) of a given classical finite binary string s. In the standard way, K(s) is defined as the length of the shortest input string for the universal self-delimiting Turing machine to output s. In the other way, we first introduce the so-called universal probability m, and then define K(s) as -log_2 m(s) without using the concept of program-size. We generalize the universal probability to a matrix-valued function, and identify this function with a POVM (positive operator-valued measure). On the basis of this identification, we study a computable POVM measurement with countable measurement outcomes performed upon a finite dimensional quantum system. We show that, up to a multiplicative constant, 2^{-K(s)} is the upper bound for the probability of each measurement outcome s in such a POVM measurement. In what follows, the upper bound 2^{-K(s)} is shown to be optimal in a certain sense.
We apply algorithmic information theory to quantum mechanics in order to shed light on an algorithmic structure which is inherent in quantum mechanics.There are two equivalent ways to define the (classical) Kolmogorov
∞mplexity K(s) of agiven classical finite binary string
s .In the standard way,
K(s) is defined as the length of the shortest input string for the universal self-delimiting Turing machine to output
s .In the other way, we first introduce the so-called universal probability
m , and then define
K(s) as
−log2m(s) without using the concept of program-size.We generalize the universal probability to amatrix-valued function, and identify this function with apositive operator-valued measure (POVM), which describes the statistics of outcomes in aquantum measurement in the general set- ting.Based on this identification, we study acomputable POVM measurement with countable measurement outcomes performed upon afinite dimensional quantum system.We show that, up to amultiplicative constant,
2−K(s) is the upper bound for the probability of each measurement outcome
s in such aquantum measurement.In what follows, the upper bound
2−K(s) is shown to be optimal in acertain sense.
In this paper we give a definition for quantum Kolmogorov complexity. In the classical setting, the Kolmogorov complexity of a string is the length of the shortest program that can produce this string as its output. It is a measure of the amount of innate randomness (or information) contained in the string. We define the quantum Kolmogorov complexity of a qubit string as the length of the shortest quantum input to a universal quantum Turing machine that produces the initial qubit string with high fidelity. The definition of P. Vitanyi (2000) measures the amount of classical information, whereas we consider the amount of quantum information in a qubit string. We argue that our definition is natural and is an accurate representation of the amount of quantum information contained in a quantum state.
Abstract This paper proposes an extension of Chaitin's halting probability Ω to a measurement operator in an infinite dimensional quantum system. Chaitin's Ω is defined as the probability that the universal self‐delimiting Turing machine U halts, and plays a central role in the development of algorithmic information theory. In the theory, there are two equivalent ways to define the program‐size complexity H (s) of a given finite binary string s. In the standard way, H (s) is defined as the length of the shortest input string for U to output s. In the other way, the so‐called universal probability m is introduced first, and then H (s) is defined as –log2 m (s) without reference to the concept of program‐size. Mathematically, the statistics of outcomes in a quantum measurement are described by a positive operator‐valued measure (POVM) in the most general setting. Based on the theory of computability structures on a Banach space developed by Pour‐El and Richards, we extend the universal probability to an analogue of POVM in an infinite dimensional quantum system, called a universal semi‐POVM. We also give another characterization of Chaitin's Ω numbers by universal probabilities. Then, based on this characterization, we propose to define an extension of Ω as a sum of the POVM elements of a universal semi‐POVM. The validity of this definition is discussed. In what follows, we introduce an operator version $ \hat H $(s) of H (s) in a Hilbert space of infinite dimension using a universal semi‐POVM, and study its properties. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
This paper proposes an extension of Chaitin's halting probability 0to measurement operator in
aan infinite dimensional quantum system.Chaitin's
Ω is defined as the probability that the universal self-delimiting Turing machine
U halts, and plays acentral role in the development of algorithmic information theory.Iri the theory, there are two equivalent ways to define the program-size complexity
H(s) of agiven finite binary string
s .In the standard way,
H(s) is defined as the length of the shortest input string for
U to output 8.In the other way, the s0-called universal probability
ΠL is introduced first, and then
H(s) is defined as
−log2m(IF) without reference to the concept of program-size.Mathematically, the statistics of outcomes in aquantum measurement are described by apositive operator-valued measure (POVM) in the most general setting.Based on the theory of computability structures on aBanach space developed by Pour-El and Richards, we extend the universal probability to an analogue of POVM in an infinite dimensional quantum system, called universal semi-POVM.We also give another characterization of Chaitin's
Ω numbers by universal probabilities.Then, based on this characterization, we propose to define an extension of
Ω as asum of the POVM elements of auniversal semi-POVM.The validity of this definition is discussed.1Introduction Algorithmic information theory is afiamework to apply information-theoretic and probabilistic ideas to recursive function theory.One of the primary concepts of algorithmic information theory is the program-size
compl6ffi˙∈y (or Kolmogorov complez-
i˙ty)H{s ) of afinite binary string
s , which is defined as the length of the shortest binary input
The purpose of this thesis is to give a formal definition of quantum Kolmogorov complexity (QC), and rigorous mathematical proofs of its basic properties. The definition used here is similar to that by Berthiaume, van Dam, and Laplante. It defines the QC of some indeterminate-length qubit string ρas the minimum base length of any quantum input which makes a quantum Turing machine (QTM) halt and output ρ, up to some error tolerance. First, we prove that there is a QTM which is universal in the sense of input base length. Furthermore, we show several general properties of QTMs, including a result on mutually orthogonal ``halting spaces'', and a way to transform every almost-halting input into a deterministically-halting input by adding at most a constant number of qubits. Afterwards, we apply these results to QC. In particular, we show that QC is invariant, incompressible, agrees with classical Kolmogorov complexity for classical strings, and is closely related to von Neumann entropy for ergodic quantum information sources.
We give a definition for the Kolmogorov complexity of a pure quantum state. In classical information theory, the algorithmic complexity of a string is a measure of the information needed by a universal machine to reproduce the string itself. We define the complexity of a quantum state by means of the classical description complexity of an (abstract) experimental procedure that allows us to prepare the state with a given fidelity. We argue that our definition satisfies the intuitive idea of complexity as a measure of "how difficult" it is to prepare a state. We apply this definition to give an upper bound on the algorithmic complexity of a number of known states. Furthermore, we establish a connection between the entanglement of a quantum state and its algorithmic complexity.